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Part III
From Computer Towards the Clinical
Chapter 15
Molecular Modeling Strategies in Drug
Design, Development, and Discovery
Targeting Proteases
Viviane Corrêa Santos , Lucas Abreu Diniz ,
and Rafaela Salgado Ferreira
Abstract This chapter explores the critical role of proteases in human metabolism
and the survival of pathogenic organisms, emphasizing their significance as drug
development targets due to their biological importance. It outlines the standard
nomenclature proposed by Schechter and Berger for categorizing protease subsites
and substrates, which aids in understanding protease specificity and guiding inhibitor design. Despite advancements in developing potent peptidic protease inhibitors,
challenges related to pharmacokinetic properties have led researchers to employ
various drug design techniques. The chapter specifically examines marketed drugs
targeting proteases for three viral diseases and Type 2 diabetes, highlighting the
importance of computational tools in drug discovery campaigns and showcasing
diverse design strategies, mainly structure-based drug design (SBDD) of
peptidomimetics and macrocyclic inhibitors.
Keywords Proteases · SBDD · Drug discovery · Viral infections · Diabetes type 2
V. C. Santos
Laboratório de Modelagem Molecular e Planejamento de Fármacos, Departamento de
Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas
Gerais, Belo Horizonte, MG, Brazil
Department of Chemistry, Grand Valley State University, Allendale, MI, USA
L. A. Diniz · R. S. Ferreira (
Laboratório de Modelagem Molecular e Planejamento de Fármacos, Departamento de
Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas
Gerais, Belo Horizonte, MG, Brazil
e-mail: rafaelasf@icb.ufmg.br
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug
Design, Computer-Aided Drug Discovery and Design 3,
https://doi.org/10.1007/978-3-031-76718-0_15
✉)
435
436 V. C. Santos et al.
Fig. 15.1 Schematic
representation of the
nomenclature for protease
substrate’s residues into
their corresponding binding
sites. Protease subsites are
named S3 to S3’ and the
substrate residues, P3 to P3’.
The scissile bond is
represented by
(Figure prepared with
ChemDraw)
⸾
1 Introduction
Proteases cleave peptidic bonds in peptides and proteins, catalyzing a hydrolysis
reaction [1]. They represent approximately 2% of the human genome and are
involved in numerous processes essential for human metabolism and homeostasis
[2]. Similarly, pathogenic organisms also rely on proteases for their survival and
replication [3]. The biological importance of this class of enzymes is one of the
reasons that so many proteases have been targeted for drug development [1–5].
Schechter and Berger proposed a standard nomenclature for protease subsites and
their substrates [6, 7], in which the amino acid residues are numbered sequentially
from the scissile bond. Toward the N-terminal of the peptide, their numbering starts
from P1, while toward the C-terminal, it begins with P1’. Protease subsites are
designated analogously: the S1 subsite is the region where the P1 motif binds, S2
recognizes P2, and so on (Fig. 15.1). This widely adopted nomenclature facilitates
comparisons of protease active sites and their specificity, which is important for
understanding their biological importance and guiding drug design.
The development of protease inhibitors frequently starts with an understanding of
their substrate specificity, which allows the design of inhibitors. For instance,
combining peptides with a warhead in an appropriate position for a nucleophilic
attack by the catalytic residue quickly yields potent peptidic protease modulators.
Despite the efficiency of this strategy, peptidic protease inhibitors freque ntly have
inadequate pharmacokinetic properties, such as low bioavailability and poor metabolic profiles [8]. These limitations have motivated the application of varied drug
design techniques to develop protease inhibitors as drug candidates.
In this chapter, we focus on four diseases (three viral diseases and Diabetes type
2) for which marketed drugs targeting proteases have been successfully developed
using computer-aided approaches (Table 15 .1 ). Among the available drugs, we find
examples of various compound classes, including peptidomimetics, macrocyclic
inhibitors, and diverse scaffolds of synthetic origin. For each disease, we discuss
the importance of the protease target and highlight the drug discovery campaigns in
which computational tools played an essential role. Instead of aiming for a
15 Molecular Modeling Strategies in Drug Design, Development, and... 437
Table 15.1 Protease inhibitors employed in treating HIV, COVID-19, Hepatitis C, and Diabetes
type 2, developed aided by computational methods
Disease Protease target Drug
HIV
infection
COVID-19M
HepatitisCNS3/4a protease Zepartier
Diabetes
type 2
HIV protease Crixivan
Kaletra
ritonavir)
pro
Dipeptidyl peptidase IV
(DPP-4)
Prezista
Paxlovid
(nirmatrelvir/ritonavir)
Xocova
elbasvir)
Vosevi
sofosbuvir/ velpatasvir)
Tenelia
®
(indinavir) Molecular modeling [9]
®
(lopinavir/
®
(darunavir) Molecular modeling [11]
®
®
(ensitrelvir) Docking-based virtual screening,
®
(grazoprevir/
®
(voxilaprevir/
®
(teneligliptin) Docking [16]
Computational methods (with
references)
Molecular modeling [10]
Docking [12]
structural pharmacophores, docking
[13]
Molecular modeling [14]
Molecular modeling [15]
comprehensive description of drugs that act on prote ases, we hope to illustrate
multiple design strategies and how computational methods have been effectively
incorporated into drug design.
2 Structure-Based Drug Design of HIV Protease Inhibitors
2.1 HIV-1 Protease as a Therapeutic Target
Acquired Immunodeficiency Syndrome (AIDS) is a global epidemic impacting
millions of people, caused by the Human Immunodeficiency Virus (HIV).
According to the Joint United Nations Program on HIV/AIDS (UNAIDS), since
the first case was reported in 1981 [17], an estimated 85.6 million people have been
infected with HIV, and approximately 40.4 million have died from AIDS or AIDSassociated deaths. Currently, an estimated 39 million people globally are living with
HIV/AIDS [18].
The discovery of HIV as the causative agent of AIDS enabled the understanding
of the molecular processes associated with HIV infection and replication in host cells
and the identification of targets for developing antiviral therapies. Of the 15 proteins
encoded by the HIV genome, integrase, reverse transcriptase, and protease act as
enzymes with specialized functions [19]. The HIV protease is an aspartic protease
that catalyzes the hydrolysis of Gag and Gag-Pol polyproteins. The cleavage of these
polyproteins generates structural proteins of the virus core and viral enzymes, which
438 V. C. Santos et al.
are incorporated into new viral particles, making this enzyme a plausible drug target
[20]. The development of HIV-1 protease inhibitors led to the approval of several
drugs included in the highly active antiretroviral therapy (HAART) in 1996
[21]. This approach consists of a multidrug regimen with different drug class es to
help avoid drug resi stance that would be selected by single monotherapies.
Structure-based drug design (SBDD) studies have been employed to develop
most protease inhibitors using computational methods and experimental data. Since
HIV-1 protease is a homodimer, residues in chain B are herein referred to as having
an * to their numbers. Here we discuss the development of first- and secondgeneration HIV protease inhibitors, as well as the strategies to address the problem
of drug resistance.
2.2 Development of First-Generation HIV Protease
Inhibitors by Structure-Based Drug Design
2.2.1 Saquinavir
The first FDA-approved HIV protease inhibitor, saquinavir, was developed based on
the transition-state isostere strategy aided by computational structure-based techniques. In this strategy, the cleavable amide is replaced by a non-hydrolyzable
tetrahedral isostere, such as hydroxyethylene or hydroxyethylamine moieties, to
generate inhibitors. The selectivity of saquinavir is based on the difference in
substrate specificity of the HIV protease and mammalian aspartic proteases. While
the host enzymes do not exhibit affinity for substrates containing proline at P1’,
sequences containing proline in the P1 ’ position are preferentially cleaved by HIV
protease, such as in the substrates featuring Phe-Pro and Tyr-Pro within the Gag and
Gag-Pol gene products [ 22 ].
During the development of saquinavir, compounds containing the
hydroxyethylamine moiety were synthesized, conducting an exhaustive exploration
of side chains and substituents with distinct steric and electronic properties. Among
the explored options, the substitution of the native proline with the decahydroisoquinoline-3-carbonyl (DIQ) group resulted in a potent HIV protease inhibitor
with an inhibition constant (K
which was later named saquinavir (Fig. 15.2a)[23]. Subsequently, crystallographic
analyses revealed that the DIQ group occupies the S1’ subsite, establishing hydrophobic interactions with the enzyme, and the carbonyl group of DIQ interacts with a
water molecule connecting the inhibitors with the flap regions. Furthermore, other
hydrogen bonds were observed between the inhibitor and the enzyme. For example,
the P2 carboxamide interacts with Asp29 and Asp30 residues (Fig. 15.2b)[24]. Ulti-
mately, in 1995, saquinavir was the first HIV protease inhibitor approved by the
FDA, as Invirase
®
[25].
) of 0.12 nM for HIV-1 and Ki< 0.1 nM for HIV-2,
i
15 Molecular Modeling Strategies in Drug Design, Development, and... 439
2.2.2 Indinavir
In general, peptidomimetic compounds exhibit poor solubility in water and low oral
bioavailability, as observed with saquinavir, which has an oral bioavailab ility of 4%
[26]. To address this issue, researchers initiated the development of indinavir from a
series of hydroxyethylene dipeptide isostere inhibitors of HIV protease that, despite
being potent HIV-1 protease inhi bitors with half-maximal inhibitory concentration
(IC
) between 0.03 nM and 470 nM, exhibited low water solubility [27]. Consider-
50
ing the structural characteristics of saquinavir, it was hypothesized that introducing
the basic group amine to this series of compounds could enhance bioavailability.
Consequently, a novel class of hydroxylamine pentanamide compounds was proposed, and molecular modeling studies were conducted to assess their potential as
HIV protease inhibitors [9].
The modeling began with the alignment of an energy-minimized structure of
HIV-1 protease complexed with a hydroxyethylene isostere inhibitor (compound
L-685,434; Fig. 15.2c) with the X-ray crystallography structure of saquinavir in
complex with HIV-1 protease. Both compounds occupied the hydrophobic binding
pockets spanning from S2 to S2’ with their P2 and P1’ carbonyl groups oriented
toward the structural water found in most X-ray crystal structures of HIV-1 inhibitors (PDB IDs: 4HVP, 5HVP, 9HVP, and 7HVP) [28–31], presumably establishing
a hydrogen bond between them. Subsequently, they overlaid the structure with the
hydroxyethylene isostere inhibitor and an energy-minimized structure for the
Fig. 15.2 First-generation HIV-1 protease inhibitors. Chemical structures of saquinavir (a) and its
binding mode (PDB ID: 1HXB) in the HIV-1 protease active site (b). Chemical structures of the
hydroxyethylene isostere inhibitor, L-685,434 (c), the hydroxylamine pentanamide inhibitor, compound 1 (d), and the approved drug, indinavir (e). Water molecules are represented as red spheres,
and the hydrogen bonds are shown as yellow dashed lines. Residues in chain B are identified by the
letter and number followed by *. Important residues and compounds are represented as sticks and
colored by atom. Carbons are colored gray in the enzyme and green in the compound.
(Figure produced using ChemDraw and PyMOL)