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\begin{document}

\title{Modeling of protein complexes in CASP14 with emphasis on the interaction
interface prediction}



\author[1]{Justas Dapkunas}%
\author[2]{Kliment Olechnovič}%
\author[2]{Česlovas Venclovas}%
\affil[1]{Vilnius University}%
\affil[2]{Villnius University}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\selectlanguage{english}
\begin{abstract}
The goal of CASP experiments is to monitor the progress in the protein
structure prediction field. During the 14th CASP edition we aimed to
test our capabilities of predicting structures of protein complexes. Our
protocol for modeling protein assemblies included both template-based
modeling and free docking. Structural templates were identified using
sensitive sequence-based searches. If sequence-based searches failed, we
performed structure-based template searches using selected CASP server
models. In the absence of reliable templates we applied free docking
starting from monomers generated by CASP servers. We evaluated and
ranked models of protein complexes using an improved version of protein
structure quality assessment method, VoroMQA, taking into account both
interaction interface and global structure scores. If reliable templates
could be identified, generally accurate models of protein assemblies
were generated with the exception of an antibody-antigen interaction.
The success of free docking mainly depended on the accuracy of initial
subunit models and on the scoring of docking solutions. To put our
overall results in perspective, we analyzed our performance in the
context of other CASP groups. Although the subunits in our assembly
models often were not of the top quality, these models had, overall, the
best predicted interfaces according to several protein-protein interface
accuracy measures. Since we did not use co-evolution-based prediction of
inter-chain contacts, we attribute our relative success in predicting
interfaces primarily to the emphasis on the interaction interface when
modeling and scoring.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

In recent years the progress in there-dimensional (3D) protein structure
prediction was impressive\textsuperscript{1}. Application of deep
learning-based methods now allows modeling of structures for most of the
individual proteins\textsuperscript{2--4}. However, the majority of
proteins do not function in isolation. They usually perform their
functions by interacting with other proteins and assembling into stable
or transient protein complexes. Therefore, if we wish to have a detailed
understanding of how proteins function, the knowledge of the structures
of individual proteins is not sufficient. We need to know the structures
of corresponding protein complexes.

The number of possible binary protein-protein interactions is much
higher than the number of proteins encoded in genomes, and only a small
part of these interactions have already been discovered
experimentally\textsuperscript{5, 6}. Similarly, the number of different
structural types of protein complexes is predicted to be much higher
than the number of protein folds\textsuperscript{7, 8}. Therefore, the
structural modeling of protein-protein interactions represents a more
complex problem than the prediction of structures for individual
proteins.

Currently, two main methods are available to model the structures of
protein complexes. Template-based modeling is based on the observation
that homologous proteins often interact in the same
way\textsuperscript{9}. Thus a known structure of a protein complex can
serve as a template for modeling homologous protein complexes. If there
are no templates, protein-protein docking methods can be
used\textsuperscript{5}. Docking methods aim to find how proteins
interact with each other starting from known structures of individual
subunits that can be either solved experimentally or obtained by
computational modeling.

The field of protein structure prediction is monitored in the Critical
Assessment of Structure Prediction (CASP) experiments that explore every
aspect of protein structure modeling\textsuperscript{1}. The Critical
Assessment of PRedicted Interactions (CAPRI) experiments are devoted to
the prediction of the structures for diverse protein
complexes\textsuperscript{10}. Both CASP and CAPRI are based on blind
testing. The participants are given the sequences of proteins, for which
structures are solved experimentally but not published (termed
``targets''), and then they are asked to provide structural models.
Subsequent comparison of models with the experimental structures enables
establishing the current state-of-the-art in the field and also
objective comparison of different methods. In recent years, CASP and
CAPRI experiments are collaborating in the area of structural modeling
of protein complexes\textsuperscript{11, 12}, and a category dedicated
to assessment of multimeric proteins has been established in CASP as
well\textsuperscript{13, 14}.

We participated in recent CASP and CAPRI experiments, aiming to test our
abilities to predict structures of protein complexes using
template-based modeling and free docking\textsuperscript{15--17}. Our
results demonstrated that there is room for improvement in both of the
methods. In template-based modeling, the identification of templates can
be enhanced. In docking, the assessment and selection of correct
interfaces from thousands of diverse docking models is probably the most
important problem. It is also interesting to see how the progress in
protein structure prediction influences modeling of protein complexes.
At present, it is often possible to generate sufficiently accurate
models of individual proteins, but does this help to predict the
protein-protein interaction interfaces?

To explore these questions in detail, we participated in the CASP14
experiment, where our group (``Venclovas'') performed relatively well,
particularly in the interaction interface prediction. In this article we
describe our modeling methods and analyze our results in detail aiming
to understand what went right, what went wrong and why.

\section*{Methods}

{\label{methods}}

\emph{Modeling outline}

The outline of our modeling workflow in CASP14 (Figure 1) was largely
the same as in CASP13\textsuperscript{17}, but with several
improvements. During the initial step, for every target we attempted to
identify multimeric templates for comparative (homology) modeling using
sequence- and structure-based search methods. If the identified
templates were not reliable, we used template-based docking. Coiled-coil
protein structures were predicted using a custom designed procedure. If
no templates could be identified, we switched to free docking. In the
case of large multisubunit targets, we combined all the methods,
employing comparative modeling approach for the parts of the complex for
which templates were available followed by docking of the resulting
subcomplexes to obtain the full assembly.

\emph{Comparative modeling}

The outline of multimeric comparative modeling pipeline is provided in
Supplementary Figure S1. Starting with target sequences, we first
searched for potential templates using PPI3D\textsuperscript{18} and
HHpred\textsuperscript{19, 20} web servers. If this step failed to
identify reliable templates, we additionally employed structure-based
searches. In this case, selected monomeric CASP server models
corresponding to the subunits of a target protein complex were used as
queries for PDB searches using the DALI server\textsuperscript{21}. The
aim was to identify multimeric PDB assemblies that could potentially
serve as multimeric templates. Once templates were identified using
either sequence- or structure-based searches, structural models for the
whole protein complex were generated by MODELLER\textsuperscript{22} and
its plugin AltMod\textsuperscript{23} using the multi-chain modeling
function.

\emph{Template-based docking}

Some targets produced only low-reliability HHpred or DALI results, yet
we observed that the identified proteins had similar functional
annotations as the CASP targets. If the alignment of CASP server models
to these multimeric structures using TM-align\textsuperscript{24} seemed
plausible, we simply aligned CASP server models to different chains of
the templates and then relaxed the resulting models to remove structural
clashes using the same methods as in the case of free docking.

\emph{Coiled-coil modeling}

Several targets were predicted to be coiled-coils using
MultiCoil2\textsuperscript{25} and similar sequence analysis tools
provided in MPI Bioinformatics Toolkit\textsuperscript{19}. Such targets
were modeled using a custom designed procedure: structure models were
automatically generated by MODELLER/AltMod using the same manually
selected coiled-coil template and all possible target-template
alignments followed by model selection.

\emph{Free docking}

When no templates could be found for protein complexes, free docking of
top 5 selected monomeric CASP server models was done by
Hex\textsuperscript{26} for hetero-complexes and
Sam\textsuperscript{27}for homomultimers (Supplementary Figure S2). The
resulting models were ranked using global and interaction interface
VoroMQA scores as described below. Next, top 100-500 models were relaxed
by a very short molecular dynamics simulation using OpenMM
software\textsuperscript{28}, Amber99SB force field and GBSA-OBC
solvation model\textsuperscript{29, 30}. The relaxed models were
subsequently re-ranked using the same scoring procedure and clustered
according to the interface CAD-score\textsuperscript{31, 32} values
aiming to select a diverse set of models.

\emph{Hybrid modeling}

For some large target protein complexes structural templates were
available only for some of the subunits or domains. In these cases, a
hybrid modeling strategy was used, that is, part of the complex was
modeled using comparative modeling whereas remaining subunits were
docked to it either by template-based docking using
TM-align\textsuperscript{24} or by free docking. In addition to that, a
mixture of free docking and template-based models was submitted for
several smaller targets that had templates only from structure-based
search.

\emph{Model selection}

For model selection we used both global structure scores and interaction
interface scores. This approach was described
previously\textsuperscript{15, 17} and implemented in the VoroMQA web
server\textsuperscript{33}, but for CASP14 we introduced some
modifications. These included VoroMQA-dark, a new method for global
structure evaluation (see below), and an improved tournament-based
ranking algorithm (see Supplementary information for details). When
ranking models based on their pairwise comparisons, the algorithm puts
more emphasis on the interaction interface pseudo-energy and less
emphasis on the global structure score. This is achieved by using a
tolerance value when comparing global VoroMQA-dark scores. If the
difference between global scores is small, the interface scoring becomes
the only decisive factor.

In addition to fully automated scoring methods, models were also
evaluated according to constraints obtained from the literature or from
the CASP contact prediction servers, if such data were available. All
models were visually inspected before submission and manual ranking
adjustments were introduced, if necessary.

\emph{VoroMQA-dark method for model quality assessment}

VoroMQA-dark is a new model quality assessment method based on the
previously published VoroMQA\textsuperscript{34} method (which will be
referred to as VoroMQA-light). VoroMQA-dark uses a neural network (NN)
trained to predict local (per-residue)
CAD-score\textsuperscript{31}values. The global structure score is
computed by averaging the predicted local scores. The NN input vector
for each residue is computed from the Voronoi tessellation-based contact
areas and the corresponding contact potential values from VoroMQA-light.
See Supplementary information for more details on VoroMQA-dark. The
VoroMQA-dark standalone software is included in the extension of the
Voronota\textsuperscript{35} package freely available from
https://kliment-olechnovic.github.io/voronota/expansion\_js/.

\section*{Results}

{\label{results}}

\emph{Overview of the results}

To analyze our performance in CASP14 we used several accuracy measures
designed to evaluate various features of multimeric models. For the
overall model evaluation we used QS-score, a distance-based measure of
interface accuracy\textsuperscript{36}. To make qualitative model
accuracy assignments we converted QS-score values to CAPRI-like accuracy
categories\textsuperscript{37}. Four other scores were used to assess
the interface and the overall structure accuracy. Interface Contact
Similarity (ICS or F1-score) and Interface Patch Similarity (IPS or
Jaccard coefficient) were used to assess contact and interface patch
prediction, respectively\textsuperscript{13}. Oligomeric lDDT and
TM-score were used to assess overall structure accuracy. LDDT is an
all-atom superposition-free score\textsuperscript{38}, whereas TM-score
is based on the rigid body superposition of C\selectlanguage{greek}α \selectlanguage{english}atoms\textsuperscript{39,
40}. In addition to the above scores reported by the Prediction Center,
we also used CAD-score\textsuperscript{31, 32} to evaluate the accuracy
of both structure and interface accuracy.

The summary of our modeling results based on QS-score is presented in
Table 1, whereas the detailed accuracy evaluation of our best models is
provided in Supplementary Table S1. As can be seen in Table 1, for 11
targets we identified multimeric templates and used comparative
modeling, producing medium or high-accuracy models for 8 of them. In the
absence of reliable target-template sequence-structure alignments, we
applied template-based docking using TM-align. This approach resulted in
models of medium accuracy for 2 targets. Hybrid approaches utilizing
both comparative modeling and docking steps were used for 9 targets with
relative success. The results of free docking were ranging from
completely incorrect to medium accuracy models. The custom modeling
procedure that we used for coiled-coils did not produce any reliable
models.

\emph{Modeling results in the context of other CASP14 groups and
automated model selection}

The results, presented in Table 1 and Supplementary Table S1, do not
tell much about our relative success. To investigate our performance in
the CASP14 context, we compared our results (group ``Venclovas'') with
those of three other top performing groups for models designated as
first (model 1). We also included our automatic scoring method
(``VoroMQA-select-new'') as a virtual group, allowing it to make
selections from all CASP14 multimeric models (produced by both automatic
servers and human groups). By doing this we aimed to test the
effectiveness of our automatic model scoring method in the best possible
scenario. For the performance comparison, we used the sum of z-scores of
two interface accuracy measures (ICS and IPS) and two global structure
accuracy measures (lDDT and TM-score) (Figure 2).

The comparison revealed that different features of our models were
predicted with different level of success. According to the accuracy of
intersubunit interfaces (ICS and IPS) we achieved the best results. In
particular, we were successful in predicting interface patches (IPS),
whereas the prediction of specific residue-residue contacts (ICS) was
somewhat less successful. On the other hand, the global structure
accuracy of our models is not so great compared to other top performing
groups. This is especially visible if we consider lDDT, an all-atom
score, largely reflecting the accuracy of individual subunits.
Interestingly, our automatic model selection method showed relatively
strong performance, taking the third position by any of the four scores.
Although this method performed worse than our human group on both
interface accuracy measures and TM-score, the results according to
all-atom accuracy (lDDT) were quite a bit better.

To look at different features in more detail, we examined per-target
z-scores. Z-score values were accumulated progressively for targets
ordered by the best ICS achieved by any group, which can be interpreted
as an estimate of the target difficulty. Figure 3 shows the resulting
plots for the models designated as first (model 1). In addition to the
data for the same top groups and ''VoroMQA-select-new'', the plots also
include the data for the best models provided by any predictor group.
The latter curve may be considered as a reference by representing the
upper limit of what could have been achieved in CASP14.

Interestingly, the per-target analysis (Fig. 3) revealed that the
relative success of different groups was dependent not only on the
evaluation measure as seen in Fig. 2, but also on the prediction
targets. According to the interface prediction accuracy, our group
dominated for most of the targets (Fig. 3A,B). On the other hand, if we
consider the global accuracy of models the picture is different.
According to TM-score (Fig. 3D) our models are below the
state-of-the-art for about half of targets, whereas according to lDDT
(Fig. 3C) this is true for nearly all the targets. To see whether our
models as assessed by lDDT were indeed significantly inferior to those
of other top groups, we examined the cumulative raw values
(Supplementary Figure S3). Surprisingly, it turned out that the absolute
differences between the groups, especially if evaluated using lDDT (Fig.
S3F), are relatively small. This indicates that in most cases subunit
structures were of comparable accuracy and that relatively large z-score
differences resulted from small structural improvements. The same
analysis performed with the CAD-score-based analogs of ICS, IPS and lDDT
scores led to similar conclusions (Supplementary Figure S4).

In addition to individual scores, we analyzed their combinations
reflecting either the interface prediction accuracy or the accuracy of
both the interface and the global structure. We performed this analysis
both for models designated as first (Fig. S5) and for the best-of-five
models (Fig. S6). The analysis of these combinations has further
corroborated above observations on our relative success in the interface
prediction and on target-dependent group performance. Interestingly, in
the analysis of best-of-five models our automatic selection method
(VoroMQA-select-new) was the best according to the interface accuracy
(Fig S6A,C) and close to the top according to the combined accuracy (Fig
S6B,D). Although having access to all the models VoroMQA-select-new had
an important advantage over other groups, the results suggest that this
automatic selection procedure is quite robust.

\emph{Template-based modeling}

As the structures of protein complexes are often evolutionary
conserved\textsuperscript{9}, template-based modeling is currently the
most reliable method to model them. Straightforward multimeric
comparative modeling resulted in medium to high accuracy models for 8 of
11 CASP14 targets. Template-based docking also resulted in medium
accuracy models for two targets. Thus, if reliable templates were
available the template-based approach worked well for both homomers and
heteromers.

Identifying the correct template having the same oligomeric state is the
key to successful modeling of protein complexes\textsuperscript{15--17,
36}. Ambiguous oligomeric state of templates may be the reason why we
failed to model T1034, for which we used templates having different
oligomeric states.

In CASP14 we had additional examples demonstrating the limitations of
template-based modeling for protein complexes. One such example, H1036,
represents a trimeric viral protein bound to an antibody. Our models
were based on homologous trimer structures bound to antibodies. This
resulted in good models of homotrimer interfaces, but the antibody was
bound to a completely different epitope (Fig. S7). This incorrectly
predicted interface is not surprising bearing in mind the nature of
antibody-antigen interactions. The binding site in the antibody
(paratope) is formed by hypervariable loop regions, and the antigen
binding site (epitope) can be anywhere on the protein
surface\textsuperscript{41}.

Our results for T1099 show another limitation of template-based approach
for protein complexes. This target is a large viral capsid, yet modeling
its structure can be reduced to a problem of predicting a homotetramer
(T1099v0) having two different interaction interfaces (T1099v1 and
T1099v2). Our models contained high-accuracy interface 2, yet the
interface 1 was incorrect (Fig. S8). The reason of this failure was the
large insertion in the target interface, compared to the template
structures.

\emph{Hybrid modeling of large targets}

When modeling a large protein complex templates might be available just
for some parts of it. During CASP14 this was the case for heteromeric
targets H1060 and H1097. For both of these targets more accurate models
were generated for those parts that had templates.

H1060 was a viral protein complex containing 27 subunits (5 homomeric
rings bound to each other, A6B3C12D6, Fig. 4). We found templates for
all the rings, and generated models of medium accuracy for all of them
using either comparative modeling or template-based docking methods.
Next, we tried to do template-based docking of the ring models to each
other using another viral template, but for such a large complex the
docking was complicated. As a result, the accuracy of hetero-complex
(H1060v1) model is lower.

Similar situation was observed with hetero-pentamer H1097, where we
tried to dock the domains of the 5\textsuperscript{th} subunit to a
homology model of a hetero-tetramer albeit with limited success
(Supplementary Table S2).

\emph{Free docking}

Our free docking results show similar trends as in previous CASP and
CAPRI experiments (Fig. 5, Supplementary Table S3)\textsuperscript{17}.
First, the IPS values are sometimes much higher than ICS. This indicates
that the residues mediating protein-protein binding are predicted better
than the mutual subunit orientation defining the exact contacts at the
interaction interface. Another observation is that the accuracy of
subunits matters a lot when docking modeled protein structures. We did
not produce any acceptable accuracy models when we were starting from
subunit structures of lower accuracy (lDDT \textless{} 0.4 or TM-score
\textless{} 0.5). It is also important to note that the opposite is not
necessarily true. Accurate models of individual subunits do not
guarantee accurate docking models.

The most successful docking results were obtained for H1081v0, T1083
(Fig. 6) and T1084. Interestingly, in the cases of T1083 and T1084 the
free docking models were better than the template-based models, but the
reasons for this are not clear. H1081 was a large target, where two
decameric rings had to be docked, and for that we developed a custom
procedure. The homology models of decameric rings were aligned on the
axis perpendicular to the ring plane and then pushed to each other
(using 1 \selectlanguage{ngerman}Å steps) and rotated around the axis (using 2 degrees steps),
saving every distinct arrangement. Afterwards all obtained models were
relaxed, scored and ranked. This custom ``two-ring docking'' procedure
resulted in surprisingly good models.

The modeling of other docking targets was less successful (Fig. 7)
illustrating common problems related to the monomer model accuracy and
scoring. For example, in the monomer structure of T1054 which we used
for docking, the position of N-terminal helix is not compatible with the
dimeric structure. The helix is too well packed against the subunit
structure, occupying the place of a helix from another subunit in the
dimer (Fig. 7A, B). Therefore, it was impossible to obtain a correct
docking model starting from such a monomer. Interestingly, when during
the post-CASP analysis we repeated the docking experiment using the same
monomer model, but without the N-terminal helix, the docking was highly
successful (Fig. 7C). Of note, the structure and the oligomeric state of
this protein are dynamic. It exists as a monomer, a dimer or a decamer
in solution, and the dimer observed in the crystal might represent an
intermediate state in decamerization\textsuperscript{42}.

In our best model for heterodimeric target H1065 one of the subunits is
rotated \textasciitilde{}180 degrees compared to the experimental
structure (Fig. 7E). Again, the interface patch is identified correctly
while the interface contacts and subunit orientation are different. Both
monomer models are fairly accurate (lDDT~\textgreater{}~0.65,
CAD-score~\textgreater{}~0.7, TM-score~\textgreater{}~0.8), therefore,
their accuracy probably is not the reason of incorrect docking (Fig 7D).
However, scoring is really problematic for H1065: both the global and
the interface VoroMQA scores of the experimental structure and the model
are highly similar (global scores: 0.70 vs. 0.68, interface energy: -354
vs. -388 for target and model, respectively). In other words, had the
experimental structure been present together with all the models it
would not necessarily have been selected.

\section*{Discussion}

{\label{discussion}}

During CASP14 our group used well known methodologies for structural
modeling of protein complexes: template-based modeling and rigid body
docking. We did not use any deep learning-based inter-chain contacts
prediction or refinement using extensive molecular dynamics simulations.
Our main aim was accurate prediction of protein-protein interfaces, even
if this meant lower global accuracy of models. As the interface accuracy
of our models was the best among CASP14 groups predicting protein
assemblies (Fig. 2, Fig. 3A,B), it appears that we have coped with this
task quite successfully. Probably the main reasons for the successful
modeling were (1) effective multimeric template identification by
sequence and structure-based methods, (2) model selection procedure,
involving improved VoroMQA scoring with more emphasis on the interaction
interface, and (3) short molecular dynamics simulations aimed at
removing unrealistic geometry and clashes in docking models.

Unlike the interaction interface accuracy, the global accuracy of our
models was not the highest (Fig. 2, Fig. 3C,D). This is particularly
evident from the close to average values of lDDT, the score that
considers all atoms. Most of these lower scores came from template-based
models generated using MODELLER/AltMod. When we used CASP server models
for docking, lDDT scores were typically higher. This suggests that had
we used more advanced modeling techniques\textsuperscript{43}, our
template-based models might have been of higher global accuracy.

The template-based modeling remains the most accurate method to predict
the structures of protein complexes, but the limiting factor for this
approach is the detection of structural templates. Typically templates
are identified by sequence-based search methods such as BLAST,
PSI-BLAST\textsuperscript{44} or HHpred\textsuperscript{20}. In CASP14,
aiming to expand the set of available templates, we additionally
employed structure-based searches. The efficiency of structure-based
approach has been greatly increased by the recent advances in protein
structure prediction\textsuperscript{3, 4}. The availability of more
accurate models for monomers may be the reason why our structure-based
template searches successfully complemented sequence-based searches in
CASP14, but less so in CASP13\textsuperscript{17}. It is possible that
the structure-based template identification for protein complexes may
play even a more prominent role in the future.

The template-based modeling of protein complexes represents a more
complex problem than the homology modeling of individual proteins.
Unlike for monomeric proteins, the modeling of complexes has to deal
with additional complications such as the presence of alternative
interaction interfaces and differences in stoichiometry of homologous
protein complexes\textsuperscript{15--17, 36}. In CASP14, the modeling
of evolutionary non-conserved antibody-antigen interactions was yet
another example of a more complex problem. In other cases such as
host-pathogen interactions that do not always emerge from long
coexistence of species, it might also be hard to apply either
template-based or co-evolution-based modeling methods.

When there are no templates and other constraints are lacking, free
docking is the only feasible approach to predict the structures of
protein complexes. Our CASP14 results support previous observations that
docking may be successful only when subunits are sufficiently
accurate\textsuperscript{11, 17}. Thus, recent breakthrough in protein
structure prediction might help not only to detect templates for
multimeric structures through structure-based searches, but also to
expand the applicability of protein docking. However, even if fairly
accurate structures of monomers are available, the docking is much
better in predicting protein-protein binding sites\textsuperscript{45,
46} than the exact mutual arrangement and interface contacts. This has
been observed by us both in previous studies\textsuperscript{17} and in
CASP14.

CASP14 results showed that our docking workflow still has a lot of room
for improvement. With more time and more computational resources devoted
for every target, some improvements could be made even while staying in
the realm of rigid body docking and keeping our current, admittedly
imperfect scoring function: (1) using more different input monomers, and
generating even more monomers by modeling domain motions and by
remodeling flexible loops and tails; (2) ensuring that the docking
software always perform a sufficiently exhaustive sampling of
conformations; (3) producing structural variations of each docking
solution using molecular dynamics or other sampling techniques. These
enhancements would allow to explore the conformational space more
thoroughly, possibly leading to better results\textsuperscript{47}.

Despite limitations of our CASP14 modeling protocol, our strong
performance suggests that the prediction of inter-chain contacts using
co-evolution and deep learning methods still has little impact on
modeling of protein-protein interactions. Why is that? Apparently, there
are multiple reasons why inter-chain contact prediction is harder than
intra-chain. For example, contact prediction for heteromeric protein
complexes requires generating joined multiple sequence alignments. The
interacting proteins in the alignment are inferred by genomic distances
or by phylogeny\textsuperscript{48, 49}, as well as selected using
automated sequence comparison procedures\textsuperscript{50}. However,
this significantly reduces the number of sequences in the alignment and
does not guarantee correct pairing of proteins. The alignment joining
problem is not present for homo-multimers, yet in this case the problem
is to distinguish intra-subunit from inter-subunit
contacts\textsuperscript{49}. So far this problem has been solved by
including the monomer structures into the prediction
pipeline\textsuperscript{51}.

In addition to the issues related to obtaining and analyzing the
multiple sequence alignments, training of supervised learning-based
methods for contact prediction using the structures of protein complexes
may be limited by the availability of experimental structural data. The
number of possible protein complexes is believed to outnumber the number
of possible protein folds\textsuperscript{7, 8}, and it is not clear
whether known structures represent a significant part of all interaction
types\textsuperscript{8, 52}. Moreover, there are examples of
protein-protein interactions such as antibody-antigen or host-pathogen
protein interactions, for which principles of co-evolution are hardly
applicable.

To conclude, the progress in monomeric protein structure prediction has
not yet translated into similar breakthrough in structural modeling of
protein complexes. A number of issues of both technical and fundamental
nature have to be solved to make a leap in producing reliable structural
models of protein interactions, and it will be exciting to see what
developments will occur in this research area in the nearest future.

\section*{Acknowledgments}

{\label{acknowledgments}}

We wish to thank the CASP organizers for making CASP14 happen, the
independent assessors for their efforts in evaluating the results and
the experimentalists for providing unpublished 3D structures as
prediction targets.

\textbf{Funding}

Research Council of Lithuania (grants S-MIP-17-60 and S-MIP-21-35).

\section*{References}

{\label{references}}

1. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical
assessment of methods of protein structure prediction (CASP)-Round XIII.
Proteins 2019;87(12):1011--1020.

2. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C,
\selectlanguage{polish}Ž\selectlanguage{ngerman}ídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K,
Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D.
Protein structure prediction using multiple deep neural networks in the
13th Critical Assessment of Protein Structure Prediction (CASP13).
Proteins 2019;87(12):1141--1148.

3. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C,
\selectlanguage{polish}Ž\selectlanguage{ngerman}ídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K,
Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D.
Improved protein structure prediction using potentials from deep
learning. Nature 2020;577(7792):706--710.

4. Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D.
Improved protein structure prediction using predicted interresidue
orientations. Proc Natl Acad Sci U S A 2020;117(3):1496--1503.

5. Keskin O, Tuncbag N, Gursoy A. Predicting Protein-Protein
Interactions from the Molecular to the Proteome Level. Chem Rev
2016;116(8):4884--4909.

6. Luck K, Kim D-K, Lambourne L, Spirohn K, Begg BE, Bian W, Brignall R,
Cafarelli T, Campos-Laborie FJ, Charloteaux B, Choi D, Coté AG, Daley M,
Deimling S, Desbuleux A, Dricot A, Gebbia M, Hardy MF, Kishore N, Knapp
JJ, Kovács IA, Lemmens I, Mee MW, Mellor JC, Pollis C, Pons C,
Richardson AD, Schlabach S, Teeking B, Yadav A, Babor M, Balcha D, Basha
O, Bowman-Colin C, Chin S-F, Choi SG, Colabella C, Coppin G, D'Amata C,
De Ridder D, De Rouck S, Duran-Frigola M, Ennajdaoui H, Goebels F,
Goehring L, Gopal A, Haddad G, Hatchi E, Helmy M, Jacob Y, Kassa Y,
Landini S, Li R, Lieshout N van, MacWilliams A, Markey D, Paulson JN,
Rangarajan S, Rasla J, Rayhan A, Rolland T, San-Miguel A, Shen Y,
Sheykhkarimli D, Sheynkman GM, Simonovsky E, Ta\selectlanguage{polish}ş\selectlanguage{english}an M, Tejeda A, Tropepe
V, Twizere J-C, Wang Y, Weatheritt RJ, Weile J, Xia Y, Yang X,
Yeger-Lotem E, Zhong Q, Aloy P, Bader GD, De Las Rivas J, Gaudet S, Hao
T, Rak J, Tavernier J, Hill DE, Vidal M, Roth FP, Calderwood MA. A
reference map of the human binary protein interactome. Nature
2020;580(7803):402--408.

7. Aloy P, Russell RB. Ten thousand interactions for the molecular
biologist. Nat Biotechnol 2004;22(10):1317--1321.

8. Garma L, Mukherjee S, Mitra P, Zhang Y. How many protein-protein
interactions types exist in nature? PLoS One 2012;7(6):e38913.

9. Aloy P, Ceulemans H, Stark A, Russell RB. The relationship between
sequence and interaction divergence in proteins. J Mol Biol
2003;332(5):989--998.

10. Lensink MF, Nadzirin N, Velankar S, Wodak SJ. Modeling
protein-protein, protein-peptide, and protein-oligosaccharide complexes:
CAPRI 7th edition. Proteins 2020;88(8):916--938.

11. Lensink MF, Velankar S, Kryshtafovych A, Huang S-Y,
Schneidman-Duhovny D, Sali A, Segura J, Fernandez-Fuentes N, Viswanath
S, Elber R, Grudinin S, Popov P, Neveu E, Lee H, Baek M, Park S, Heo L,
Rie Lee G, Seok C, Qin S, Zhou H-X, Ritchie DW, Maigret B, Devignes M-D,
Ghoorah A, Torchala M, Chaleil RAG, Bates PA, Ben-Zeev E, Eisenstein M,
Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F,
Guerois R, Vangone A, Rodrigues JPGLM, Zundert G van, Nellen M, Xue L,
Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X,
Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim H-R,
Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC,
Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda
T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jim\selectlanguage{ngerman}énez-García B,
Moal IH, Férnandez-Recio J, Joung JY, Kim JY, Joo K, Lee J, Kozakov D,
Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T,
Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ,
Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ. Prediction of
homoprotein and heteroprotein complexes by protein docking and
template-based modeling: A CASP-CAPRI experiment. Proteins 2016;84 Suppl
1:323--348.

12. Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG,
Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu R-R, Xu
X-M, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka
E, Lipska A, Krupa P, Mozolewska M, Golon \selectlanguage{polish}Ł, \selectlanguage{english}Samsonov S, Liwo A,
Crivelli S, Pag\selectlanguage{ngerman}ès G, Karasikov M, Kadukova M, Yan Y, Huang S-Y, Rosell
M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L,
Fernandez-Recio J, Christoffer C, Terashi G, Shin W-H, Aderinwale T,
Maddhuri Venkata Subraman SR, Kihara D, Kozakov D, Vajda S, Porter K,
Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie
DW, Chauvot de Beauchêne I, Maigret B, Devignes M-D, Ruiz Echartea ME,
Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M,
Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D,
Dapk\selectlanguage{polish}ū\selectlanguage{english}nas J, Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č, \selectlanguage{english}Kundrotas PJ, Belkin S,
Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T,
Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J,
Ryan Merideth B, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J,
Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L,
Jim\selectlanguage{ngerman}énez-García B, Noort CW van, Honorato RV, Bonvin AMJJ, Wodak SJ.
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI
experiment. Proteins 2019;87(12):1200--1221.

13. Lafita A, Bliven S, Kryshtafovych A, Bertoni M, Monastyrskyy B,
Duarte JM, Schwede T, Capitani G. Assessment of protein assembly
prediction in CASP12. Proteins 2018;86 Suppl 1:247--256.

14. Guzenko D, Lafita A, Monastyrskyy B, Kryshtafovych A, Duarte JM.
Assessment of protein assembly prediction in CASP13. Proteins
2019;87(12):1190--1199.

15. Dapk\selectlanguage{polish}ū\selectlanguage{english}nas J, Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}Modeling of protein complexes
in CAPRI Round 37 using template-based approach combined with model
selection. Proteins 2018;86 Suppl 1:292--301.

16. Dapk\selectlanguage{polish}ū\selectlanguage{english}nas J, Kairys V, Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}Template-based
modeling of diverse protein interactions in CAPRI rounds 38-45. Proteins
2020;88(8):939--947.

17. Dapk\selectlanguage{polish}ū\selectlanguage{english}nas J, Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}Structural modeling of
protein complexes: Current capabilities and challenges. Proteins
2019;87(12):1222--1232.

18. Dapk\selectlanguage{polish}ū\selectlanguage{english}nas J, Timinskas A, Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Margelevi\selectlanguage{polish}č\selectlanguage{english}ius M, Di\selectlanguage{polish}č\selectlanguage{english}i\selectlanguage{polish}ū\selectlanguage{english}nas R,
Venclovas \selectlanguage{polish}Č. \selectlanguage{english}The PPI3D web server for searching, analyzing and modeling
protein-protein interactions in the context of 3D structures.
Bioinformatics 2017;33(6):935--937.

19. Zimmermann L, Stephens A, Nam S-Z, Rau D, K\selectlanguage{ngerman}übler J, Lozajic M,
Gabler F, Söding J, Lupas AN, Alva V. A Completely Reimplemented MPI
Bioinformatics Toolkit with a New HHpred Server at its Core. J Mol Biol
2018;430(15):2237--2243.

20. Söding J. Protein homology detection by HMM-HMM comparison.
Bioinformatics 2005;21(7):951--960.

21. Holm L. DALI and the persistence of protein shape. Protein Sci
2020;29(1):128--140.

22. \selectlanguage{polish}Š\selectlanguage{english}ali A, Blundell TL. Comparative Protein Modelling by Satisfaction
of Spatial Restraints. J Mol Biol 1993;234(3):779--815.

23. Janson G, Grottesi A, Pietrosanto M, Ausiello G, Guarguaglini G,
Paiardini A. Revisiting the ``satisfaction of spatial restraints''
approach of MODELLER for protein homology modeling. PLoS Comput Biol
2019;15(12):e1007219.

24. Zhang Y, Skolnick J. TM-align: a protein structure alignment
algorithm based on the TM-score. Nucleic Acids Res
2005;33(7):2302--2309.

25. Trigg J, Gutwin K, Keating AE, Berger B. Multicoil2: predicting
coiled coils and their oligomerization states from sequence in the
twilight zone. PLoS One 2011;6(8):e23519.

26. Ritchie DW, Kemp GJ. Protein docking using spherical polar Fourier
correlations. Proteins 2000;39(2):178--194.

27. Ritchie DW, Grudinin S. Spherical polar Fourier assembly of protein
complexes with arbitrary point group symmetry. J Appl Cryst
2016;49(1):158--167.

28. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA,
Wang L-P, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR,
Pande VS. OpenMM 7: Rapid development of high performance algorithms for
molecular dynamics. PLoS Comput Biol 2017;13(7):e1005659.

29. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C.
Comparison of multiple Amber force fields and development of improved
protein backbone parameters. Proteins 2006;65(3):712--725.

30. Onufriev A, Bashford D, Case DA. Exploring protein native states and
large-scale conformational changes with a modified generalized born
model. Proteins 2004;55(2):383--394.

31. Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Kulberkyt\selectlanguage{polish}ė \selectlanguage{english}E, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}CAD-score: a new contact
area difference-based function for evaluation of protein structural
models. Proteins 2013;81(1):149--162.

32. Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}Contact Area-Based Structural Analysis of
Proteins and Their Complexes Using CAD-Score. Methods Mol Biol
2020;2112:75--90.

33. Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}VoroMQA web server for assessing
three-dimensional structures of proteins and protein complexes. Nucleic
Acids Res 2019;47(W1):W437--W442.

34. Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}VoroMQA: Assessment of protein structure
quality using interatomic contact areas. Proteins 2017;85(6):1131--1145.

35. Olechnovi\selectlanguage{polish}č \selectlanguage{english}K, Venclovas \selectlanguage{polish}Č. \selectlanguage{english}Voronota: A fast and reliable tool for
computing the vertices of the Voronoi diagram of atomic balls. J Comput
Chem 2014;35(8):672--681.

36. Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling
protein quaternary structure of homo- and hetero-oligomers beyond binary
interactions by homology. Sci Rep 2017;7(1):10480.

37. M\selectlanguage{ngerman}éndez R, Leplae R, De Maria L, Wodak SJ. Assessment of blind
predictions of protein-protein interactions: current status of docking
methods. Proteins 2003;52(1):51--67.

38. Mariani V, Biasini M, Barbato A, Schwede T. lDDT: a local
superposition-free score for comparing protein structures and models
using distance difference tests. Bioinformatics 2013;29(21):2722--2728.

39. Zhang Y, Skolnick J. Scoring function for automated assessment of
protein structure template quality. Proteins 2004;57(4):702--710.

40. Mukherjee S, Zhang Y. MM-align: a quick algorithm for aligning
multiple-chain protein complex structures using iterative dynamic
programming. Nucleic Acids Res 2009;37(11):e83.

41. Sela-Culang I, Kunik V, Ofran Y. The structural basis of
antibody-antigen recognition. Front Immunol 2013;4:302.

42. Grinter R, Morris FC, Dunstan RA, Leung PM, Belousoff M, Gunasinghe
SD, Beckham S, Peleg AY, Greening C, Li J, Heinz E, Lithgow T. BonA from
Acinetobacter baumannii forms a divisome-localized decamer that supports
outer envelope function. bioRxiv 2020:2020.09.01.278697.

43. Song Y, DiMaio F, Wang RY-R, Kim D, Miles C, Brunette T, Thompson J,
Baker D. High-resolution comparative modeling with RosettaCM. Structure
2013;21(10):1735--1742.

44. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W,
Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein
database search programs. Nucleic Acids Res 1997;25(17):3389--3402.

45. Fernández-Recio J, Totrov M, Abagyan R. Identification of
protein-protein interaction sites from docking energy landscapes. J Mol
Biol 2004;335(3):843--865.

46. Viswanathan R, Fajardo E, Steinberg G, Haller M, Fiser A.
Protein-protein binding supersites. PLoS Comput Biol
2019;15(1):e1006704.

47. Kurkcuoglu Z, Bonvin AMJJ. Pre- and post-docking sampling of
conformational changes using ClustENM and HADDOCK for protein-protein
and protein-DNA systems. Proteins 2020;88(2):292--306.

48. Zeng H, Wang S, Zhou T, Zhao F, Li X, Wu Q, Xu J. ComplexContact: a
web server for inter-protein contact prediction using deep learning.
Nucleic Acids Res 2018;46(W1):W432--W437.

49. Szurmant H, Weigt M. Inter-residue, inter-protein and inter-family
coevolution: bridging the scales. Curr Opin Struct Biol 2018;50:26--32.

50. Faure G, Andreani J, Guerois R. InterEvol database: exploring the
structure and evolution of protein complex interfaces. Nucleic Acids Res
2012;40(Database issue):D847-856.

51. Uguzzoni G, John Lovis S, Oteri F, Schug A, Szurmant H, Weigt M.
Large-scale identification of coevolution signals across homo-oligomeric
protein interfaces by direct coupling analysis. Proc Natl Acad Sci U S A
2017;114(13):E2662--E2671.

52. Ahnert SE, Marsh JA, Hernández H, Robinson CV, Teichmann SA.
Principles of assembly reveal a periodic table of protein complexes.
Science 2015;350(6266):aaa2245.

\section*{Tables}

{\label{tables}}

Table 1. Summary of our CASP14 assembly modeling results\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Modeling strategy & Number of targets & Accuracy category of our best
model & Accuracy category of our best model & Accuracy category of our
best model & Accuracy category of our best model\tabularnewline
\midrule
\endhead
& & High (QS-score [?] 0.7) & Medium (0.3 [?] QS-score \textless{} 0.7) &
Low (0.1 [?] QS-score \textless{} 0.3) & Incorrect (QS-score \textless{}
0.1)\tabularnewline
Comparative modeling & 11 & 1 & 7 & 0 & 3\tabularnewline
Template-based docking & 2 & 0 & 2 & 0 & 0\tabularnewline
Hybrid & 9 & 1 & 3 & 5 & 0\tabularnewline
Free docking & 5 & 0 & 1 & 2 & 2\tabularnewline
Coiled-coil modeling & 2 & 0 & 0 & 1 & 1\tabularnewline
Total & 29 & 2 & 13 & 8 & 6\tabularnewline
\bottomrule
\end{longtable}

\section*{Figure legends}

{\label{figure-legends}}

\textbf{Figure 1} . Summary of modeling workflow in CASP14.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2} . Comparison of results of our group (``Venclovas'')
and our automated model selection method (``VoroMQA-select-new'') with
other top performing CASP14 groups.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3} . Cumulative z-score values of the models designated
as first, targets were ordered by the maximum achieved ICS score.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 4} . Modeling of H1060. In the images of subcomplexes
v1-v5 the target structure is shown in gray and the model structure is
colored.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 5} . Dependence of the predicted interface accuracy on
the subunit accuracy in our free docking models having the largest ICS
or IPS scores.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 6} . Successful docking models for H1081 and T1083.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 7} . Free docking results for T1054 and H1065; A, a
monomer model of T1054 has additional folded helix in the N-terminus
(blue, encircled); B, a dimer model (interface CAD-score 0, binding site
CAD-score 0.34), in which the N-terminal helix (encircled) occupies the
place of another subunit; C, a dimer model (interface CAD-score 0.33,
binding site CAD-score 0.58), in which a monomer without N-terminus was
used for docking; D, monomer models of H1065; E, interface patch is
predicted better than the interface contacts for H1065 because subunit 2
(red) is rotated \textasciitilde{}180 degrees in the best model of
H1065. Target structures are grey, model structures are colored in all
images.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

